Title: The failure prediction models: a comparative study

Authors: Nesrine Ayadi

Addresses: University of Sfax, Sfax, Tunisia

Abstract: The objective of this paper is to explain and predict the companies' failure risk of claiming a credit in order to improve the decision-making process. Our tests are conducted on a sample of 513 French companies and use the methodologies of principal component analysis, Fisher's linear discriminant analysis; and logistic regression. All have been implemented using carefully selected economic and financial ratios. The empirical results show that the turnover in logarithm, financial independence, the by-employee turnover and the turnover growth rate are the most important explanatory variables with a ranking success that exceeds 70%. This finding is consistent across all tested models and specifications.

Keywords: credit risk; failure prediction; Fisher linear discriminant analysis; logistic regression; credit scoring.

DOI: 10.1504/IJAF.2019.106759

International Journal of Accounting and Finance, 2019 Vol.9 No.2/3/4, pp.170 - 204

Accepted: 07 May 2019
Published online: 14 Apr 2020 *

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